Loading packages¶
In [1]:
from genepy.utils import helper as h
# to comment in your case
from taigapy import TaigaClient
tc = TaigaClient()
from celligner import Celligner
import pandas as pd
# to comment in your case
from depmapomics import tracker as track
#autoreload
%load_ext autoreload
%autoreload 2
#output
from bokeh.plotting import output_notebook
output_notebook()
from celligner.params import TISSUE_COLOR
/home/jeremie/celligner/celligner/mnnpy/mnnpy/utils.py:14: NumbaWarning: Compilation is falling back to object mode WITH looplifting enabled because Function "l2_norm" failed type inference due to: No implementation of function Function(<function norm at 0x7ff92c2d32f0>) found for signature: >>> norm(x=array(float32, 2d, A), axis=Literal[int](1)) There are 2 candidate implementations: - Of which 2 did not match due to: Overload in function 'norm_impl': File: numba/np/linalg.py: Line 2352. With argument(s): '(x=array(float32, 2d, A), axis=int64)': Rejected as the implementation raised a specific error: TypeError: norm_impl() got an unexpected keyword argument 'x' raised from /home/jeremie/miniconda3/lib/python3.7/site-packages/numba/core/typing/templates.py:722 During: resolving callee type: Function(<function norm at 0x7ff92c2d32f0>) During: typing of call at /home/jeremie/celligner/celligner/mnnpy/mnnpy/utils.py (16) File "celligner/mnnpy/mnnpy/utils.py", line 16: def l2_norm(in_matrix): return np.linalg.norm(x=in_matrix, axis=1) ^ @jit(float32[:](float32[:, :]), nogil=True) /home/jeremie/miniconda3/lib/python3.7/site-packages/numba/core/object_mode_passes.py:152: NumbaWarning: Function "l2_norm" was compiled in object mode without forceobj=True. File "celligner/mnnpy/mnnpy/utils.py", line 15: @jit(float32[:](float32[:, :]), nogil=True) def l2_norm(in_matrix): ^ state.func_ir.loc)) /home/jeremie/miniconda3/lib/python3.7/site-packages/numba/core/object_mode_passes.py:162: NumbaDeprecationWarning: Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour. For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit File "celligner/mnnpy/mnnpy/utils.py", line 15: @jit(float32[:](float32[:, :]), nogil=True) def l2_norm(in_matrix): ^ state.func_ir.loc)) /home/jeremie/celligner/celligner/mnnpy/mnnpy/utils.py:14: NumbaWarning: Code running in object mode won't allow parallel execution despite nogil=True. @jit(float32[:](float32[:, :]), nogil=True) /home/jeremie/celligner/celligner/mnnpy/mnnpy/utils.py:29: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(float32, 1d, A), array(float32, 1d, A)) dist[i, j] = np.dot(m[i], n[j]) /home/jeremie/celligner/celligner/mnnpy/mnnpy/utils.py:197: NumbaWarning: Compilation is falling back to object mode WITH looplifting enabled because Function "adjust_s_variance" failed type inference due to: NameError: name 'sq_dist_to_line' is not defined @jit(float32(float32[:, :], float32[:, :], float32[:], float32[:], float32), nogil=True) /home/jeremie/celligner/celligner/mnnpy/mnnpy/utils.py:197: NumbaWarning: Compilation is falling back to object mode WITHOUT looplifting enabled because Function "adjust_s_variance" failed type inference due to: Cannot determine Numba type of <class 'numba.core.dispatcher.LiftedLoop'> File "celligner/mnnpy/mnnpy/utils.py", line 205: def adjust_s_variance(data1, data2, curcell, curvect, sigma): <source elided> totalprob2 = 0. for samecell in data2: ^ @jit(float32(float32[:, :], float32[:, :], float32[:], float32[:], float32), nogil=True) /home/jeremie/miniconda3/lib/python3.7/site-packages/numba/core/object_mode_passes.py:152: NumbaWarning: Function "adjust_s_variance" was compiled in object mode without forceobj=True, but has lifted loops. File "celligner/mnnpy/mnnpy/utils.py", line 199: def adjust_s_variance(data1, data2, curcell, curvect, sigma): distance1 = np.zeros((data1.shape[0], 2), dtype=np.float32) ^ state.func_ir.loc)) /home/jeremie/miniconda3/lib/python3.7/site-packages/numba/core/object_mode_passes.py:162: NumbaDeprecationWarning: Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour. For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit File "celligner/mnnpy/mnnpy/utils.py", line 199: def adjust_s_variance(data1, data2, curcell, curvect, sigma): distance1 = np.zeros((data1.shape[0], 2), dtype=np.float32) ^ state.func_ir.loc)) /home/jeremie/celligner/celligner/mnnpy/mnnpy/utils.py:197: NumbaWarning: Code running in object mode won't allow parallel execution despite nogil=True. @jit(float32(float32[:, :], float32[:, :], float32[:], float32[:], float32), nogil=True) /home/jeremie/celligner/celligner/mnnpy/mnnpy/utils.py:236: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(float32, 1d, C), array(float32, 1d, A)) scale = np.dot(working, grad)
Loading expression files¶
In [2]:
# load from taiga public (figshare link)
# load internal expression,
# latest version can be found at https://depmap.org/portal/download/
# can also be loaded like so pd.read_csv('gs://ccle_default_params/celligner_ex/CCLE_expression.csv.gz', index_col=0)
CCLE_expression = tc.get(name='internal-21q3-fe4c',
file='CCLE_expression_full')
# load TCGA expression
# this dataset was generated from ,using this script:
# caan be found here: pd.read_csv('gs://ccle_default_params/celligner_ex/TCGA_expression.csv.gz', index_col=0)
TCGA_expression = tc.get(name='celligner-input-9827',
file='tumor_expression')
No dataset version provided. Using version 16. No dataset version provided. Using version 1.
In [3]:
# subset gene names to ensembl ids only
CCLE_expression = CCLE_expression[CCLE_expression.columns[:-92]]
CCLE_expression.columns = list(map(lambda x: x.split(
' (')[1][:-1] if ' (' in x else x, CCLE_expression.columns))
common = set(CCLE_expression.columns).intersection(
set(TCGA_expression.columns))
CCLE_expression = CCLE_expression[list(common)]
TCGA_expression = TCGA_expression[list(common)]
Managing annotations¶
In [5]:
# loading annotations
CCLE_annotation = track.getTracker() # the function uses pygsheets to load this: REFSHEET_URL=https://docs.google.com/spreadsheets/d/1Pgb5fIClGnErEqzxpU7qqX6ULpGTDjvzWwDN8XUJKIY
# Sheets.from_files(MY_ID, MYSTORAGE_ID).get(REFSHEET_URL).sheets[0].to_frame(index_col=0)
# you can also get it from pd.read_csv('gs://ccle_default_params/celligner_ex/CCLE_annotation.csv.gz', index_col=0)
# can be loaded from
# pd.read_csv('gs://ccle_default_params/celligner_ex/TCGA_annotation.csv.gz', index_col=0)
TCGA_annotation = tc.get(name='celligner-input-9827',
file='tumor_annotations') # generated manually
No dataset version provided. Using version 1.
In [6]:
# transforming annotations
CCLE_annotation = CCLE_annotation.drop_duplicates('arxspan_id').set_index("arxspan_id")
CCLE_annotation = CCLE_annotation.loc[CCLE_expression.index, ["origin", 'subtype']].rename(columns={"origin": "tissue_type", "subtype": 'disease_type'})
CCLE_annotation["cell_type"] = "cancer cell line"
TCGA_annotation = TCGA_annotation.set_index("sampleID").loc[TCGA_expression.index,["lineage",
"subtype"]].rename(columns={"lineage":"tissue_type", "subtype": 'disease_type'})
TCGA_annotation['cell_type'] = "tumor sample"
In [81]:
# some name are not consistent between the two datasets
rename = {np.nan: "unknown", "adrenal_cortex": "adrenal", "colorectal": "colon", 'thymus': 'thyroid', 'meninges':"central_nervous_system", None: "unknown", 'brain': "central_nervous_system"}
CCLE_annotation = CCLE_annotation.replace({"tissue_type": rename})
TCGA_annotation = TCGA_annotation.replace({"tissue_type": rename})
[autoreload of celligner failed: Traceback (most recent call last):
File "/home/jeremie/miniconda3/lib/python3.7/site-packages/IPython/extensions/autoreload.py", line 245, in check
superreload(m, reload, self.old_objects)
File "/home/jeremie/miniconda3/lib/python3.7/site-packages/IPython/extensions/autoreload.py", line 394, in superreload
module = reload(module)
File "/home/jeremie/miniconda3/lib/python3.7/imp.py", line 314, in reload
return importlib.reload(module)
File "/home/jeremie/miniconda3/lib/python3.7/importlib/__init__.py", line 169, in reload
_bootstrap._exec(spec, module)
File "<frozen importlib._bootstrap>", line 630, in _exec
File "<frozen importlib._bootstrap_external>", line 724, in exec_module
File "<frozen importlib._bootstrap_external>", line 860, in get_code
File "<frozen importlib._bootstrap_external>", line 791, in source_to_code
File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed
File "/home/jeremie/celligner/celligner/__init__.py", line 729
for val in
^
SyntaxError: invalid syntax
]
[autoreload of celligner.params failed: Traceback (most recent call last):
File "/home/jeremie/miniconda3/lib/python3.7/site-packages/IPython/extensions/autoreload.py", line 245, in check
superreload(m, reload, self.old_objects)
File "/home/jeremie/miniconda3/lib/python3.7/site-packages/IPython/extensions/autoreload.py", line 394, in superreload
module = reload(module)
File "/home/jeremie/miniconda3/lib/python3.7/imp.py", line 314, in reload
return importlib.reload(module)
File "/home/jeremie/miniconda3/lib/python3.7/importlib/__init__.py", line 169, in reload
_bootstrap._exec(spec, module)
File "<frozen importlib._bootstrap>", line 630, in _exec
File "<frozen importlib._bootstrap_external>", line 724, in exec_module
File "<frozen importlib._bootstrap_external>", line 860, in get_code
File "<frozen importlib._bootstrap_external>", line 791, in source_to_code
File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed
File "/home/jeremie/celligner/celligner/params.py", line 4
'other',
^
SyntaxError: invalid syntax
]
Fitting celliner with the CCLE dataset¶
In [ ]:
# issues when rerunning celligner
In [17]:
my_alligner = Celligner(make_plots=True, priotize_fit=True)
my_alligner.fit(CCLE_expression, CCLE_annotation)
fetching gene names from biomart cache
using only usefull genes
looking at 1411 samples.
found 29593 common genes
creating a fit dataset..
reducing dimensionality...
clustering...
WARNING: You’re trying to run this on 29593 dimensions of `.X`, if you really want this, set `use_rep='X'`.
Falling back to preprocessing with `sc.pp.pca` and default params.
doing differential expression analysis on the clusters running differential expression on 34 clusters running limmapy on the samples you need to have R installed with the limma library installed 3.4.5 done
Out[17]:
<celligner.Celligner at 0x7f2d848adf98>
In [10]:
# running with regular mnn
my_alligner.method = "mnn"
_ = my_alligner.transform(TCGA_expression, TCGA_annotation)
looking at 12236 samples.
found 29593 common genes
creating a transform input..
reducing dimensionality...
clustering..
WARNING: You’re trying to run this on 70 dimensions of `.X`, if you really want this, set `use_rep='X'`.
Falling back to preprocessing with `sc.pp.pca` and default params.
doing differential expression analysis on the clusters.. running differential expression on 58 clusters running limmapy on the samples you need to have R installed with the limma library installed 3.4.5 there is 0.398 overlap between the fit and transform dataset in their most variable genes doing cPCA.. transform regressing out the cPCA components.. doing the MNN analysis using scanPy MNN... Performing cosine normalization...
/home/jeremie/celligner/celligner/mnnpy/mnnpy/utils.py:14: NumbaWarning: Compilation is falling back to object mode WITH looplifting enabled because Function "l2_norm" failed type inference due to: No implementation of function Function(<function norm at 0x7ffb24068598>) found for signature: >>> norm(x=array(float32, 2d, A), axis=Literal[int](1)) There are 2 candidate implementations: - Of which 2 did not match due to: Overload in function 'norm_impl': File: numba/np/linalg.py: Line 2352. With argument(s): '(x=array(float32, 2d, A), axis=int64)': Rejected as the implementation raised a specific error: TypeError: norm_impl() got an unexpected keyword argument 'x' raised from /home/jeremie/miniconda3/lib/python3.7/site-packages/numba/core/typing/templates.py:722 During: resolving callee type: Function(<function norm at 0x7ffb24068598>) During: typing of call at /home/jeremie/celligner/celligner/mnnpy/mnnpy/utils.py (16) File "celligner/mnnpy/mnnpy/utils.py", line 16: def l2_norm(in_matrix): return np.linalg.norm(x=in_matrix, axis=1) ^ @jit(float32[:](float32[:, :]), nogil=True) /home/jeremie/miniconda3/lib/python3.7/site-packages/numba/core/object_mode_passes.py:152: NumbaWarning: Function "l2_norm" was compiled in object mode without forceobj=True. File "celligner/mnnpy/mnnpy/utils.py", line 15: @jit(float32[:](float32[:, :]), nogil=True) def l2_norm(in_matrix): ^ state.func_ir.loc)) /home/jeremie/miniconda3/lib/python3.7/site-packages/numba/core/object_mode_passes.py:162: NumbaDeprecationWarning: Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour. For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit File "celligner/mnnpy/mnnpy/utils.py", line 15: @jit(float32[:](float32[:, :]), nogil=True) def l2_norm(in_matrix): ^ state.func_ir.loc)) /home/jeremie/celligner/celligner/mnnpy/mnnpy/utils.py:14: NumbaWarning: Code running in object mode won't allow parallel execution despite nogil=True. @jit(float32[:](float32[:, :]), nogil=True) /home/jeremie/celligner/celligner/mnnpy/mnnpy/utils.py:14: NumbaWarning: Compilation is falling back to object mode WITH looplifting enabled because Function "l2_norm" failed type inference due to: No implementation of function Function(<function norm at 0x7ffb24068598>) found for signature: >>> norm(x=array(float32, 2d, A), axis=Literal[int](1)) There are 2 candidate implementations: - Of which 2 did not match due to: Overload in function 'norm_impl': File: numba/np/linalg.py: Line 2352. With argument(s): '(x=array(float32, 2d, A), axis=int64)': Rejected as the implementation raised a specific error: TypeError: norm_impl() got an unexpected keyword argument 'x' raised from /home/jeremie/miniconda3/lib/python3.7/site-packages/numba/core/typing/templates.py:722 During: resolving callee type: Function(<function norm at 0x7ffb24068598>) During: typing of call at /home/jeremie/celligner/celligner/mnnpy/mnnpy/utils.py (16) File "celligner/mnnpy/mnnpy/utils.py", line 16: def l2_norm(in_matrix): return np.linalg.norm(x=in_matrix, axis=1) ^ @jit(float32[:](float32[:, :]), nogil=True) /home/jeremie/miniconda3/lib/python3.7/site-packages/numba/core/object_mode_passes.py:152: NumbaWarning: Function "l2_norm" was compiled in object mode without forceobj=True. File "celligner/mnnpy/mnnpy/utils.py", line 15: @jit(float32[:](float32[:, :]), nogil=True) def l2_norm(in_matrix): ^ state.func_ir.loc)) /home/jeremie/miniconda3/lib/python3.7/site-packages/numba/core/object_mode_passes.py:162: NumbaDeprecationWarning: Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour. For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit File "celligner/mnnpy/mnnpy/utils.py", line 15: @jit(float32[:](float32[:, :]), nogil=True) def l2_norm(in_matrix): ^ state.func_ir.loc)) /home/jeremie/celligner/celligner/mnnpy/mnnpy/utils.py:14: NumbaWarning: Code running in object mode won't allow parallel execution despite nogil=True. @jit(float32[:](float32[:, :]), nogil=True)
Starting MNN correct iteration. Reference batch: 0 Step 1 of 1: processing batch 1 Looking for MNNs... found 10135 mnns.. Computing correction vectors... Adjusting variance... Applying correction... MNN correction complete. Gathering output... done reducing dimensionality... > /home/jeremie/celligner/celligner/__init__.py(630)plot() 629 import ipdb; ipdb.set_trace() --> 630 if 'colors' not in plot_kwargs: 631 if show_clusts: ipdb> c [1, 1, 1, 1, 1, 1, 1, 1, 1] making plot...
In [29]:
# running with regular mnn
my_alligner.method = "mnn"
_ = my_alligner.transform(_rerun=False)
reducing dimensionality...
doing differential expression analysis on the clusters.. regressing out the cPCA components.. doing the MNN analysis using scanPy MNN... Performing cosine normalization...
/home/jeremie/celligner/celligner/mnnpy/mnnpy/utils.py:14: NumbaWarning: Compilation is falling back to object mode WITH looplifting enabled because Function "l2_norm" failed type inference due to: No implementation of function Function(<function norm at 0x7f48a80eaa60>) found for signature: >>> norm(x=array(float32, 2d, A), axis=Literal[int](1)) There are 2 candidate implementations: - Of which 2 did not match due to: Overload in function 'norm_impl': File: numba/np/linalg.py: Line 2352. With argument(s): '(x=array(float32, 2d, A), axis=int64)': Rejected as the implementation raised a specific error: TypeError: norm_impl() got an unexpected keyword argument 'x' raised from /home/jeremie/miniconda3/lib/python3.7/site-packages/numba/core/typing/templates.py:722 During: resolving callee type: Function(<function norm at 0x7f48a80eaa60>) During: typing of call at /home/jeremie/celligner/celligner/mnnpy/mnnpy/utils.py (16) File "celligner/mnnpy/mnnpy/utils.py", line 16: def l2_norm(in_matrix): return np.linalg.norm(x=in_matrix, axis=1) ^ @jit(float32[:](float32[:, :]), nogil=True) /home/jeremie/miniconda3/lib/python3.7/site-packages/numba/core/object_mode_passes.py:152: NumbaWarning: Function "l2_norm" was compiled in object mode without forceobj=True. File "celligner/mnnpy/mnnpy/utils.py", line 15: @jit(float32[:](float32[:, :]), nogil=True) def l2_norm(in_matrix): ^ state.func_ir.loc)) /home/jeremie/miniconda3/lib/python3.7/site-packages/numba/core/object_mode_passes.py:162: NumbaDeprecationWarning: Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour. For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit File "celligner/mnnpy/mnnpy/utils.py", line 15: @jit(float32[:](float32[:, :]), nogil=True) def l2_norm(in_matrix): ^ state.func_ir.loc)) /home/jeremie/celligner/celligner/mnnpy/mnnpy/utils.py:14: NumbaWarning: Code running in object mode won't allow parallel execution despite nogil=True. @jit(float32[:](float32[:, :]), nogil=True) /home/jeremie/celligner/celligner/mnnpy/mnnpy/utils.py:14: NumbaWarning: Compilation is falling back to object mode WITH looplifting enabled because Function "l2_norm" failed type inference due to: No implementation of function Function(<function norm at 0x7f48a80eaa60>) found for signature: >>> norm(x=array(float32, 2d, A), axis=Literal[int](1)) There are 2 candidate implementations: - Of which 2 did not match due to: Overload in function 'norm_impl': File: numba/np/linalg.py: Line 2352. With argument(s): '(x=array(float32, 2d, A), axis=int64)': Rejected as the implementation raised a specific error: TypeError: norm_impl() got an unexpected keyword argument 'x' raised from /home/jeremie/miniconda3/lib/python3.7/site-packages/numba/core/typing/templates.py:722 During: resolving callee type: Function(<function norm at 0x7f48a80eaa60>) During: typing of call at /home/jeremie/celligner/celligner/mnnpy/mnnpy/utils.py (16) File "celligner/mnnpy/mnnpy/utils.py", line 16: def l2_norm(in_matrix): return np.linalg.norm(x=in_matrix, axis=1) ^ @jit(float32[:](float32[:, :]), nogil=True) /home/jeremie/miniconda3/lib/python3.7/site-packages/numba/core/object_mode_passes.py:152: NumbaWarning: Function "l2_norm" was compiled in object mode without forceobj=True. File "celligner/mnnpy/mnnpy/utils.py", line 15: @jit(float32[:](float32[:, :]), nogil=True) def l2_norm(in_matrix): ^ state.func_ir.loc)) /home/jeremie/miniconda3/lib/python3.7/site-packages/numba/core/object_mode_passes.py:162: NumbaDeprecationWarning: Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour. For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit File "celligner/mnnpy/mnnpy/utils.py", line 15: @jit(float32[:](float32[:, :]), nogil=True) def l2_norm(in_matrix): ^ state.func_ir.loc)) /home/jeremie/celligner/celligner/mnnpy/mnnpy/utils.py:14: NumbaWarning: Code running in object mode won't allow parallel execution despite nogil=True. @jit(float32[:](float32[:, :]), nogil=True)
Starting MNN correct iteration. Reference batch: 0 Step 1 of 1: processing batch 1 Looking for MNNs... found 10136 mnns.. Computing correction vectors... Adjusting variance... Applying correction... MNN correction complete. Gathering output... done reducing dimensionality... making plot...
In [13]:
my_alligner.umap_kwargs
Out[13]:
{'n_neighbors': 10, 'min_dist': 0.5, 'metric': 'euclidean', 'n_components': 2}
In [31]:
my_alligner.plot(color_column="tissue_type", colortable=TISSUE_COLOR, umap_kwargs={'n_neighbors': 15,'min_dist': 0.2, 'metric': 'cosine'})
reducing dimensionality... making plot...
Out[31]:
Figure(
id = '6215', …)
above = [],
align = 'start',
aspect_ratio = None,
aspect_scale = 1,
background = None,
background_fill_alpha = 1.0,
background_fill_color = '#ffffff',
below = [LinearAxis(id='6226', ...)],
border_fill_alpha = 1.0,
border_fill_color = '#ffffff',
center = [Grid(id='6229', ...), Grid(id='6233', ...)],
css_classes = [],
disabled = False,
extra_x_ranges = {},
extra_y_ranges = {},
frame_height = None,
frame_width = None,
height = 600,
height_policy = 'auto',
hidpi = True,
inner_height = 0,
inner_width = 0,
js_event_callbacks = {},
js_property_callbacks = {},
left = [LinearAxis(id='6230', ...)],
lod_factor = 10,
lod_interval = 300,
lod_threshold = 2000,
lod_timeout = 500,
margin = (0, 0, 0, 0),
match_aspect = False,
max_height = None,
max_width = None,
min_border = 5,
min_border_bottom = None,
min_border_left = None,
min_border_right = None,
min_border_top = None,
min_height = None,
min_width = None,
name = None,
outer_height = 0,
outer_width = 0,
outline_line_alpha = 1.0,
outline_line_cap = 'butt',
outline_line_color = '#e5e5e5',
outline_line_dash = [],
outline_line_dash_offset = 0,
outline_line_join = 'bevel',
outline_line_width = 1,
output_backend = 'svg',
renderers = [GlyphRenderer(id='6267', ...)],
reset_policy = 'standard',
right = [],
sizing_mode = None,
subscribed_events = [],
syncable = True,
tags = [],
title = Title(id='6216', ...),
title_location = 'above',
toolbar = Toolbar(id='6250', ...),
toolbar_location = 'right',
toolbar_sticky = True,
visible = True,
width = 600,
width_policy = 'auto',
x_range = DataRange1d(id='6218', ...),
x_scale = LinearScale(id='6222', ...),
y_range = DataRange1d(id='6220', ...),
y_scale = LinearScale(id='6224', ...))
In [28]:
my_alligner.plot(rerun=False)
making plot...
Out[28]:
Figure(
id = '4816', …)
above = [],
align = 'start',
aspect_ratio = None,
aspect_scale = 1,
background = None,
background_fill_alpha = 1.0,
background_fill_color = '#ffffff',
below = [LinearAxis(id='4827', ...)],
border_fill_alpha = 1.0,
border_fill_color = '#ffffff',
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min_border_bottom = None,
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outline_line_join = 'bevel',
outline_line_width = 1,
output_backend = 'svg',
renderers = [GlyphRenderer(id='4868', ...)],
reset_policy = 'standard',
right = [],
sizing_mode = None,
subscribed_events = [],
syncable = True,
tags = [],
title = Title(id='4817', ...),
title_location = 'above',
toolbar = Toolbar(id='4851', ...),
toolbar_location = 'right',
toolbar_sticky = True,
visible = True,
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width_policy = 'auto',
x_range = DataRange1d(id='4819', ...),
x_scale = LinearScale(id='4823', ...),
y_range = DataRange1d(id='4821', ...),
y_scale = LinearScale(id='4825', ...))
In [ ]:
my_alligner.neightbors_kwargs
In [18]:
# using the marioni mnn method
my_alligner.method = "mnn_marioni"
my_alligner.mnn_kwargs = {'k1': 5, 'k2': 50, 'cosine_norm': True, "fk":5}
_ = my_alligner.transform(TCGA_expression, TCGA_annotation)
looking at 12236 samples.
found 29593 common genes
creating a transform input..
reducing dimensionality...
clustering..
WARNING: You’re trying to run this on 70 dimensions of `.X`, if you really want this, set `use_rep='X'`.
Falling back to preprocessing with `sc.pp.pca` and default params.
doing differential expression analysis on the clusters.. running differential expression on 57 clusters running limmapy on the samples you need to have R installed with the limma library installed 3.4.5 there is 0.329 overlap between the fit and transform dataset in their most variable genes > /home/jeremie/celligner/celligner/__init__.py(416)transform() 415 # doing cPCA on the dataset --> 416 print('doing cPCA..') 417 # TODO: try the automated version, (select the best alpha above 1?) ipdb> c doing cPCA.. transform regressing out the cPCA components.. doing the MNN analysis using Marioni et al. method.. Performing cosine normalization...
/home/jeremie/celligner/celligner/mnnpy/mnnpy/utils.py:14: NumbaWarning: Compilation is falling back to object mode WITH looplifting enabled because Function "l2_norm" failed type inference due to: No implementation of function Function(<function norm at 0x7f2e97d29a60>) found for signature: >>> norm(x=array(float32, 2d, A), axis=Literal[int](1)) There are 2 candidate implementations: - Of which 2 did not match due to: Overload in function 'norm_impl': File: numba/np/linalg.py: Line 2352. With argument(s): '(x=array(float32, 2d, A), axis=int64)': Rejected as the implementation raised a specific error: TypeError: norm_impl() got an unexpected keyword argument 'x' raised from /home/jeremie/miniconda3/lib/python3.7/site-packages/numba/core/typing/templates.py:722 During: resolving callee type: Function(<function norm at 0x7f2e97d29a60>) During: typing of call at /home/jeremie/celligner/celligner/mnnpy/mnnpy/utils.py (16) File "celligner/mnnpy/mnnpy/utils.py", line 16: def l2_norm(in_matrix): return np.linalg.norm(x=in_matrix, axis=1) ^ @jit(float32[:](float32[:, :]), nogil=True) /home/jeremie/celligner/celligner/mnnpy/mnnpy/utils.py:14: NumbaWarning: Compilation is falling back to object mode WITH looplifting enabled because Function "l2_norm" failed type inference due to: No implementation of function Function(<function norm at 0x7f2e97d29a60>) found for signature: >>> norm(x=array(float32, 2d, A), axis=Literal[int](1)) There are 2 candidate implementations: - Of which 2 did not match due to: Overload in function 'norm_impl': File: numba/np/linalg.py: Line 2352. With argument(s): '(x=array(float32, 2d, A), axis=int64)': Rejected as the implementation raised a specific error: TypeError: norm_impl() got an unexpected keyword argument 'x' raised from /home/jeremie/miniconda3/lib/python3.7/site-packages/numba/core/typing/templates.py:722 During: resolving callee type: Function(<function norm at 0x7f2e97d29a60>) During: typing of call at /home/jeremie/celligner/celligner/mnnpy/mnnpy/utils.py (16) File "celligner/mnnpy/mnnpy/utils.py", line 16: def l2_norm(in_matrix): return np.linalg.norm(x=in_matrix, axis=1) ^ @jit(float32[:](float32[:, :]), nogil=True) /home/jeremie/miniconda3/lib/python3.7/site-packages/numba/core/object_mode_passes.py:152: NumbaWarning: Function "l2_norm" was compiled in object mode without forceobj=True. File "celligner/mnnpy/mnnpy/utils.py", line 15: @jit(float32[:](float32[:, :]), nogil=True) def l2_norm(in_matrix): ^ state.func_ir.loc)) /home/jeremie/miniconda3/lib/python3.7/site-packages/numba/core/object_mode_passes.py:152: NumbaWarning: Function "l2_norm" was compiled in object mode without forceobj=True. File "celligner/mnnpy/mnnpy/utils.py", line 15: @jit(float32[:](float32[:, :]), nogil=True) def l2_norm(in_matrix): ^ state.func_ir.loc)) /home/jeremie/miniconda3/lib/python3.7/site-packages/numba/core/object_mode_passes.py:162: NumbaDeprecationWarning: Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour. For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit File "celligner/mnnpy/mnnpy/utils.py", line 15: @jit(float32[:](float32[:, :]), nogil=True) def l2_norm(in_matrix): ^ state.func_ir.loc)) /home/jeremie/miniconda3/lib/python3.7/site-packages/numba/core/object_mode_passes.py:162: NumbaDeprecationWarning: Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour. For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit File "celligner/mnnpy/mnnpy/utils.py", line 15: @jit(float32[:](float32[:, :]), nogil=True) def l2_norm(in_matrix): ^ state.func_ir.loc)) /home/jeremie/celligner/celligner/mnnpy/mnnpy/utils.py:14: NumbaWarning: Code running in object mode won't allow parallel execution despite nogil=True. @jit(float32[:](float32[:, :]), nogil=True) /home/jeremie/celligner/celligner/mnnpy/mnnpy/utils.py:14: NumbaWarning: Code running in object mode won't allow parallel execution despite nogil=True. @jit(float32[:](float32[:, :]), nogil=True)
Looking for MNNs... Found 10906 mutual nearest neighbors. done reducing dimensionality... making plot...
In [22]:
my_alligner.plot(color_column="tissue_type", colortable=TISSUE_COLOR, umap_kwargs={'n_neighbors': 10,'min_dist': 0.2, 'metric': 'euclidean'})
reducing dimensionality... making plot...
Out[22]:
Figure(
id = '3067', …)
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align = 'start',
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aspect_scale = 1,
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background_fill_alpha = 1.0,
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border_fill_alpha = 1.0,
border_fill_color = '#ffffff',
center = [Grid(id='3081', ...), Grid(id='3085', ...)],
css_classes = [],
disabled = False,
extra_x_ranges = {},
extra_y_ranges = {},
frame_height = None,
frame_width = None,
height = 600,
height_policy = 'auto',
hidpi = True,
inner_height = 0,
inner_width = 0,
js_event_callbacks = {},
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min_border_bottom = None,
min_border_left = None,
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min_border_top = None,
min_height = None,
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outer_width = 0,
outline_line_alpha = 1.0,
outline_line_cap = 'butt',
outline_line_color = '#e5e5e5',
outline_line_dash = [],
outline_line_dash_offset = 0,
outline_line_join = 'bevel',
outline_line_width = 1,
output_backend = 'svg',
renderers = [GlyphRenderer(id='3119', ...)],
reset_policy = 'standard',
right = [],
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tags = [],
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title_location = 'above',
toolbar = Toolbar(id='3102', ...),
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toolbar_sticky = True,
visible = True,
width = 600,
width_policy = 'auto',
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x_scale = LinearScale(id='3074', ...),
y_range = DataRange1d(id='3072', ...),
y_scale = LinearScale(id='3076', ...))
In [52]:
my_alligner.save('../temp/demo/')
------------------------------------------------- PicklingError Traceback (most recent call last) <ipython-input-52-d7b272fd5fed> in <module> ----> 1 my_alligner.save('../temp/demo/') ~/celligner/celligner/__init__.py in save(self, folder, asData) 525 if not asData: 526 with open(os.path.join(folder, 'model.pkl'), 'wb') as f: --> 527 pickle.dump(self, f) 528 # save the data 529 else: PicklingError: Can't pickle <class 'celligner.Celligner'>: it's not the same object as celligner.Celligner
adding other datasets to celligner¶
In [48]:
# you can load the dataset from gcp: (you can do so by hand or by installing gsutil)
# (make sure you have the right folder and then do:
# ! gsutil cp gs://ccle_default_params/cellinger_ex/model.pkl ../temp/demo/
my_alligner = Celligner()
my_alligner.load('../temp/demo/')
fetching gene names from biomart cache using only usefull genes
In [3]:
# met500
met500_ann = tc.get(name='met500-fc3c', file='met500_ann')
met500_meta = tc.get(name='met500-fc3c', file='met500_meta')
met500_TPM = tc.get(name='met500-fc3c', file='met500_TPM') #20,979x868 matrix
#Novartis_PDX
Novartis_PDX_ann = tc.get(name='pdx-data-3d29', file='Novartis_PDX_ann')
Novartis_PDX_TPM = tc.get(name='pdx-data-3d29', file='Novartis_PDX_TPM').T # 38,087x445
#pediatric_PDX
pediatric_PDX_ann = tc.get(name='pdx-data-3d29', file='pediatric_PDX_ann')
pediatric_PDX_TPM = tc.get(name='pdx-data-3d29', file='pediatric_PDX_TPM') #80,000x250
No dataset version provided. Using version 1. No dataset version provided. Using version 1. No dataset version provided. Using version 1. No dataset version provided. Using version 2. No dataset version provided. Using version 2. No dataset version provided. Using version 2. No dataset version provided. Using version 2.
In [4]:
met500_meta["primary_site"] = met500_ann['primary_site'].values
del met500_ann
met500_ann = met500_meta.rename(columns={"Sample_id": 'sample_id', 'tissue': 'tissue_type', 'primary_site': "disease_type", "type": "cell_type"}).set_index('sample_id', drop=True)[["tissue_type","disease_type","cell_type"]].replace({"tissue_type":rename})
In [5]:
pediatric_PDX_ann = pediatric_PDX_ann.rename(columns={"sampleID": 'sample_id', 'lineage': 'tissue_type', 'subtype': "disease_type", "type": "cell_type"}).set_index('sample_id', drop=True)[['cell_type', 'disease_type', 'tissue_type']].replace({"tissue_type":rename})
In [6]:
Novartis_PDX_ann = Novartis_PDX_ann.rename(columns={"sampleID": 'sample_id', 'lineage': 'tissue_type', 'subtype': "disease_type", "type": "cell_type"}).set_index('sample_id', drop=True)[['cell_type', 'disease_type', 'tissue_type']].replace({"tissue_type":rename})
In [9]:
import seaborn as sns
sns.heatmap(pd.concat([pediatric_PDX_TPM.loc[:,set(pediatric_PDX_TPM.columns) & set(Novartis_PDX_TPM.columns)], Novartis_PDX_TPM.loc[:,set(pediatric_PDX_TPM.columns) & set(Novartis_PDX_TPM.columns)]]).T.corr())
Out[9]:
<AxesSubplot:>
In [53]:
# if you want to align to both CCLE and TCGA, you can ask celligner to consider the two (fit + _pre-transformed_ transform datasets) as a fit dataset by calling:
# my_alligner.putAllToFit()
# you can add your dataset as a dataset to be aligned to, by puting it in fit:
# my_alligner.addToFit(yourdataset).transform()
# /!\ need to already have a transform dataset (if you loaded the example model, this is TCGA)
# you can add your dataset as one to align, by putting it in transform:
# my_alligner.addToTransform(yourdataset)
# /!\ need to already have a fit dataset (if you loaded the example model, this is CCLE)
# if your dataset is small enough it might actually not work well to put it in transform it seems!
# if your dataset is small and similar enough, you can add the parameter dotransform=False (or dofit=False) so that it doesn't fully retransforms or refit but uses cached computation instead.
my_alligner.priotize_fit=False
my_alligner.putAllToFit(redo_diff=False)
_ = my_alligner.transform(met500_TPM, met500_ann, recompute_contamination=False)
clustering...
WARNING: You’re trying to run this on 29593 dimensions of `.X`, if you really want this, set `use_rep='X'`.
Falling back to preprocessing with `sc.pp.pca` and default params.
done
looking at 868 samples.
found 18218 common genes
creating a transform input..
clustering..
WARNING: You’re trying to run this on 18218 dimensions of `.X`, if you really want this, set `use_rep='X'`.
Falling back to preprocessing with `sc.pp.pca` and default params.
reducing dimensionality...
doing differential expression analysis on the clusters.. running differential expression on 35 clusters running limmapy on the samples you need to have R installed with the limma library installed 3.4.5 there is 0.235 overlap between the fit and transform dataset in their most variable genes regressing out the cPCA components.. doing the MNN analysis using Marioni et al. method.. Performing cosine normalization... Looking for MNNs... Found 3713 mutual nearest neighbors. done reducing dimensionality... making plot...
Out[53]:
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TP_2156-capt-SI_12477-C7G91ANXX 0.228639 0.007876
ENSG00000158716 ENSG00000108417 ... \
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ES_5004-capt-SI_5834-C19KEACXX -0.157262 1.010369 ...
ES_5004-poly-SI_5767-C19KEACXX -0.242821 0.489920 ...
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TP_2123-poly-SI_11689-C7G60ANXX -1.474862 -0.164041 ...
TP_2130-capt-SI_11905-C7FMDANXX 1.151907 0.222681 ...
TP_2131-capt-SI_11906-C7F4VANXX 1.080469 0.055891 ...
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TP_2156-capt-SI_12477-C7G91ANXX 2.362499 -0.772788 ...
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In [54]:
Novartis_PDX_TPM = Novartis_PDX_TPM.loc[:,set(Novartis_PDX_TPM)& set(pediatric_PDX_TPM)]
pediatric_PDX_TPM = pediatric_PDX_TPM.loc[:,set(Novartis_PDX_TPM)& set(pediatric_PDX_TPM)]
In [58]:
pediatric_PDX_ann['cell_type'] = "PDX_2"
In [64]:
Novartis_PDX_ann = Novartis_PDX_ann.loc[Novartis_PDX_TPM.index]
pediatric_PDX_ann = pediatric_PDX_ann.loc[pediatric_PDX_TPM.index]
In [55]:
my_alligner.putAllToFit(redo_diff=False)
clustering...
WARNING: You’re trying to run this on 18218 dimensions of `.X`, if you really want this, set `use_rep='X'`.
Falling back to preprocessing with `sc.pp.pca` and default params.
done
In [74]:
_= my_alligner.transform(pd.concat([Novartis_PDX_TPM, pediatric_PDX_TPM]), pd.concat([Novartis_PDX_ann, pediatric_PDX_ann]), recompute_contamination=False)
looking at 689 samples.
found 18049 common genes
creating a transform input..
clustering..
WARNING: You’re trying to run this on 18049 dimensions of `.X`, if you really want this, set `use_rep='X'`.
Falling back to preprocessing with `sc.pp.pca` and default params.
reducing dimensionality...
doing differential expression analysis on the clusters.. running differential expression on 22 clusters running limmapy on the samples you need to have R installed with the limma library installed 3.4.5 there is 0.208 overlap between the fit and transform dataset in their most variable genes regressing out the cPCA components.. doing the MNN analysis using Marioni et al. method.. Performing cosine normalization... Looking for MNNs... Found 3023 mutual nearest neighbors. done reducing dimensionality... making plot...
Out[74]:
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...])
In [85]:
from celligner.params import TISSUE_COLOR
In [97]:
my_alligner.plot(color_column="tissue_type", colortable=TISSUE_COLOR)
reducing dimensionality... making plot...
Out[97]:
Figure(
id = '3591', …)
above = [],
align = 'start',
aspect_ratio = None,
aspect_scale = 1,
background = None,
background_fill_alpha = 1.0,
background_fill_color = '#ffffff',
below = [LinearAxis(id='3602', ...)],
border_fill_alpha = 1.0,
border_fill_color = '#ffffff',
center = [Grid(id='3605', ...), Grid(id='3609', ...)],
css_classes = [],
disabled = False,
extra_x_ranges = {},
extra_y_ranges = {},
frame_height = None,
frame_width = None,
height = 600,
height_policy = 'auto',
hidpi = True,
inner_height = 0,
inner_width = 0,
js_event_callbacks = {},
js_property_callbacks = {},
left = [LinearAxis(id='3606', ...)],
lod_factor = 10,
lod_interval = 300,
lod_threshold = 2000,
lod_timeout = 500,
margin = (0, 0, 0, 0),
match_aspect = False,
max_height = None,
max_width = None,
min_border = 5,
min_border_bottom = None,
min_border_left = None,
min_border_right = None,
min_border_top = None,
min_height = None,
min_width = None,
name = None,
outer_height = 0,
outer_width = 0,
outline_line_alpha = 1.0,
outline_line_cap = 'butt',
outline_line_color = '#e5e5e5',
outline_line_dash = [],
outline_line_dash_offset = 0,
outline_line_join = 'bevel',
outline_line_width = 1,
output_backend = 'svg',
renderers = [GlyphRenderer(id='3643', ...)],
reset_policy = 'standard',
right = [],
sizing_mode = None,
subscribed_events = [],
syncable = True,
tags = [],
title = Title(id='3592', ...),
title_location = 'above',
toolbar = Toolbar(id='3626', ...),
toolbar_location = 'right',
toolbar_sticky = True,
visible = True,
width = 600,
width_policy = 'auto',
x_range = DataRange1d(id='3594', ...),
x_scale = LinearScale(id='3598', ...),
y_range = DataRange1d(id='3596', ...),
y_scale = LinearScale(id='3600', ...))
In [ ]: